|MIT15.084JS04 - MIT OpenCourseWare|
This course introduces students to the fundamentals of nonlinear optimization theory and methods. Topics include unconstrained and constrained optimization, linear and quadratic programming, Lagrange and conic duality theory, interior-point algorithms and theory, Lagrangian relaxation, generalized programming, and semi-definite programming. Algorithmic methods used in the class include steepest descent, Newton's method, conditional gradient and subgradient optimization, interior-point methods an...
Keywords: unconstrained and constrained optimization; unconstrained and constrained optimization; Lagrangean relaxation; generalized programming; Newton's method; conditional gradient and subgradient optimization; linear and quadratic programming; lagrange and conic duality theory; interior-point algorithms and theory; semi-definite programming; Algorithmic methods include steepest descent; interior-point methods and penalty and barrier methods; 15.084J; 6.252J; 15.084; 6.252